Browsing: Multimodal

Why Document OCR Still Remains a Hard Engineering Problem? What does it take to make OCR useful for real documents instead of clean demo images? And…

This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models…

Google expanded its Gemini model family with the release of Gemini Embedding 2. This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address…

Microsoft has released Phi-4-reasoning-vision-15B, a 15 billion parameter open-weight multimodal reasoning model designed for image and text tasks that require both perception and selective reasoning. It…

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%?…

NVIDIA has just released Dynamo v0.9.0. This is the most significant infrastructure upgrade for the distributed inference framework to date. This update simplifies how large-scale models…

section(“6) pack unpack”) B, Cemb = 2, 128 class_token = torch.randn(B, 1, Cemb, device=device) image_tokens = torch.randn(B, 196, Cemb, device=device) text_tokens = torch.randn(B, 32, Cemb, device=device)…

Google Research is proposing a new way to build accessible software with Natively Adaptive Interfaces (NAI), an agentic framework where a multimodal AI agent becomes the…

Embedding models power many modern applications—from semantic search and Retrieval-Augmented Generation (RAG) to recommendation systems and content understanding. However, selecting an embedding model requires careful consideration—after…

Samsung just dropped some juicy details about its 2026 product lineup, confirming a new foldable is set to arrive in the second half of the year.…